A new approach to seismic data integration during reservoir characterization using optimal non‐parametric transformations

Abstract

We propose a two-stage approach to integrating seismic data into reservoir characterization. First, we use a non-parametric approach to calibrate the seismic and well data through an optimal transformation to obtain the maximal correlation between two data sets. These optimal transformations are totally data-driven and do not assume any a priori functional relationship. Next, cokriging or stochastic cosimulation is carried out in the transformed space to generate conditional realizations of reservoir properties. The proposed approach allows for non-linearity between reservoir properties and seismic attributes and exploits the secondary data to its fullest potential. Furthermore, cokriging or cosimulation is considerably simplified when carried in conjunction with the optimal transformations because of a significant reduction in the variance function calculations particularly when multiple seismic attributes are involved. The proposed approach has been applied to synthetic as well as field examples. The synthetic examples involve reproducing a pre-generated primary data set using sparse primary and multiple dense secondary data sets. A comparison with traditional kriging and cokriging is also presented to illustrate the superiority of our proposed approach. The field example uses 3-D seismic and well log data from a 2 mi{sup 2} area of the Stratton gas field in South Texas --more » a fluvial reservoir system. Using multiple seismic attributes in conjunction with well data, we estimate pore-footage distribution for a selected zone in the middle Frio formation.« less